Tracking an Auto-Regressive Process with Limited Communication

Jinan, R and Parag, P and Tyagi, H (2020) Tracking an Auto-Regressive Process with Limited Communication. In: 2020 IEEE International Symposium on Information Theory, ISIT 2020, 21-26 July 2020, Los Angeles; United States, pp. 2462-2467.

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Abstract

Samples from a high-dimensional AR1 process are quantized and sent over a time-slotted communication channel of finite capacity. The receiver seeks to form an estimate of the process in real-time. We consider the slow-sampling regime where multiple communication slots occur between two sampling instants. We propose a successive update scheme which uses communication between sampling instants to update the estimates of the latest sample. We show that there exist quantizers that render the fast but loose version of this scheme, which updates estimates in every slot, universally optimal asymptotically. However, we provide evidence that most practical quantizers will require a judiciously chosen update frequency.

Item Type:	Conference Paper
Publication:	IEEE International Symposium on Information Theory - Proceedings
Publisher:	Institute of Electrical and Electronics Engineers Inc.
Additional Information:	The copyright of this article belongs to Institute of Electrical and Electronics Engineers Inc.
Keywords:	Auto regressive process; Finite capacity; High-dimensional; Limited communication; Quantizers; Real time; Sampling instants; Update schemes, Information theory
Department/Centre:	Division of Electrical Sciences > Electrical Communication Engineering
Date Deposited:	24 Sep 2020 05:59
Last Modified:	24 Sep 2020 05:59
URI:	http://eprints.iisc.ac.in/id/eprint/66616

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